Introducing Cloudera Machine Learning: Cloudera’s New Cloud-Native Platform for Enterprise Scale Machine Learning 1.15.19

1. INTRODUCING CLOUDERA MACHINE LEARNING Cloud-Native Platform for Enterprise Scale Machine Learning Tristan Zajonc, CTO for Machine Learning

2. 2 © Cloudera, Inc. All rights reserved. DISCLAIMER The information in this document is proprietary to Cloudera. No part of this document may be reproduced or disclosed without the express prior written permission of Cloudera. This document is not binding upon Cloudera in any way (including with respect to Cloudera’s course of business, product strategy, future releases, or development commitments), and is not a part of or subject to any license agreement or other agreement you may have with Cloudera. Cloudera makes no commitments about any future developments or functionality in any Cloudera product. The development, release, and timing of release of any software features or functionality described in this document remains at the discretion of Cloudera and you should not rely on any statements about development plans or anticipated functionality in this document when making any purchasing decisions. Cloudera assumes no responsibility for errors or omissions in this document. Cloudera does not warrant the accuracy or completeness of the information, text, graphics, links or other items contained within this material. This document is provided without a warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability, fitness for a particular purpose or non-infringement. Cloudera shall have no liability for damages of any kind including without limitation direct, special, indirect or consequential damages that may result from the use of these materials.”

3. Confidential-Restricted – For Discussion Purposes Only 3 © Cloudera, Inc. All rights reserved. Let’s get started. Have you • Used Spark? • Used Cloudera Data Science Workbench? • Used Kubernetes? • Used GPUs for deep learning? POLL QUESTION 1

4. 4 © Cloudera, Inc. All rights reserved. THE OPPORTUNITY: THE INDUSTRIALIZATION OF AI PROTECT business CONNECT products & services (IoT) DRIVE customer insights Thousands of use cases for ML and AI to unlock value within the enterprise ● Predictive maintenance ● Logistics optimization ● Self-driving cars ● Medical diagnostics ● Marketing effectiveness ● Next best action ● Insider threat prevention ● Fraud prevention ● Payment integrity

7. 7 © Cloudera, Inc. All rights reserved. MACHINE LEARNING AT UBER, NETFLIX, AND FACEBOOK Industrialized AI requires requires new supporting tools and platforms Facebook FBLearner Uber Michelangelo Netflix Recommendation Platform

11. 11 © Cloudera, Inc. All rights reserved. LESSON 1: ML AT SCALE REQUIRES A UNIFIED DATA STRATEGY Streaming Ingest Batch Ingest Machine Learning Tools BI Tools and SQL Editors Data Products DATA, METADATA, SECURITY, GOVERNANCE, WORKLOAD MANAGEMENT MACHINE LEARNING DATA ENGINEERING DATA WAREHOUSE OPERATIONAL DATABASE

13. Confidential-Restricted – For Discussion Purposes Only 13 © Cloudera, Inc. All rights reserved. LESSON 3: AGILITY REQUIRES SELF-SERVICE PLATFORM Manage pipelines + models Deploy models Automate pipelines Monitor performance DEPLOYDEVELOP Make teams more productive Explore data Develop reports, pipelines, models Collaborate with peers TRAIN Scale resources efficiently Train models Tune parameters Track performance End-to-end machine learning infrastructure for teams building at scale MANAGE Run anywhere with a common architecture Manage access and resources Scale cost with usage

14. Confidential-Restricted – For Discussion Purposes Only 14 © Cloudera, Inc. All rights reserved. LESSON 4: LEVERAGE THE BEST INFRASTRUCTURE FOR TASK Non-Cloud Private Cloud Public Cloud PaaS I want to maximize • Cost-efficiency • Control, elasticity, and convenience • Control, elasticity, and convenience • Agility I want to minimize • Dependence on unproven technology • Resource contention between departments • Dependence on data center floor space • Dependence on IT and therefore need as simple as possible I want to standardize • On whatever provides the best ROI • On a single environment for the entire data center • On a single cloud provider for all infrastructure needs • On whatever is easiest to use I want to store my data • On premises because cheaper and/or more secure • On premises due to company / government mandate • In the cloud because easier • In the cloud because easier

15. 15 © Cloudera, Inc. All rights reserved. TODAY: CLOUDERA DATA SCIENCE WORKBENCH Accelerate Machine Learning from Research to Production on CDH/HDP For data scientists • Experiment faster Use R, Python, or Scala with on- demand compute and secure CDH data access • Work together Share reproducible research with your whole team • Deploy with confidence Get to production repeatably and without recoding For IT professionals • Bring data science to the data Give your data science team more freedom while reducing the risk and cost of silos • Secure by default Leverage common security and governance across workloads • Run anywhere On-premises or in the cloud

16. 16 © Cloudera, Inc. All rights reserved. TODAY: CURRENT CDSW ARCHITECTURE Containerized gateway to CDH/HDP cluster for data scientists • Benefits • Easy to attach for self-service data science • Seamless connectivity to existing clusters • Isolated, reproducible user environments • Tradeoffs • Requires sizing, managing two clusters • Requires dependency coordination • Not optimized for distributed training • Complex, inelastic setup in public cloud CDH/HDP CDH/HDP Cloudera Manager / Ambari gateway node(s) CDH/HDP nodes Hive, HDFS, ... CDSW CDSW ... Master ... Engine EngineEngine EngineEngine

18. 18 © Cloudera, Inc. All rights reserved. 18 enable the end-to-end machine learning lifecycle for teams building thousands of applications across petabytes of data with an enterprise-ready platform built for the cloud Rapid provisioning and experimentation DevOps mindset From development to production Team collaboration and shared context Any framework, your data, your models Secure by default CPUs, GPUS, TPUs, and beyond Massive, diverse data Automation Runs anywhere

19. 19 © Cloudera, Inc. All rights reserved. CLOUDERA MACHINE LEARNING (PRIVATE PREVIEW) Cloud-native enterprise machine learning platform DATA SCIENCE DATA ENGINEERING MODEL OPERATIONS CLOUDERA ML RUNTIME Python/R, Spark, TensorFlow, CPU/GPU-Optimized Interactive Development Batch Pipelines Predictive APIs Full capability of CDSW Rapid cloud provisioning and elastic autoscaling Unified data engineering and ML with seamless dependency management Multi-cloud portability powered by Kubernetes Connects to HDFS or cloud object storage and shared metadata Accelerated deep learning with distributed GPU training * Initially targeted for cloud managed K8s services, then OpenShift KUBERNETES EKS, AKS, GKE, OpenShift DATA CATALOG GOVERNANCESECURITY LIFECYCLEWORKLOAD XM STORAGE Amazon S3 Microsoft ADLS HDFS KUDU

20. 20 © Cloudera, Inc. All rights reserved. Requires and extends CDH/HDP, pushing distributed compute to the cluster CDH / HDP CDH / HDP Cloudera Manager / Ambari gateway node(s) CDH/HDP nodes Hive, HDFS, ... CDSW CDSW ... Master ... Engine EngineEngine EngineEngine Cloud optimized, elastic distributed compute; can optionally use CDH / HDP Kubernetes (EKS, GKE, AKS, OpenShift) CDH / HDP CDH / HDP Hive, HDFS, HMS, Sentry, Navigator CML CML ... Master ... Engine EngineEngine EngineEngine Object Store Altus Director / CloudBreak OPTIONAL DATA SCIENCE WORKBENCH vs. CLOUDERA MACHINE LEARNING Cloud ML APIs

21. 21 © Cloudera, Inc. All rights reserved. • Lightweight form-factor optimized for cloud and private cloud • Elastic compute (CPU / GPU) in the cloud • Unified workflow for distributed data engineering and machine learning • Simple dependency management, particularly for ML workloads • python: pip install, R: install.packages, etc WHY SPARK ON KUBERNETES?

23. 23 © Cloudera, Inc. All rights reserved. from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() results = spark.sparkContext .parallelize(xrange(0, 1000)) .map(f) .collect()

25. Confidential-Restricted – For Discussion Purposes Only25 © Cloudera, Inc. All rights reserved. OPERATIONAL EXPERIENCE ✓ Simple installation and management ✓ Lower costs with elastic compute ✓ Easier to support through self-service ✓ Cloud agility everywhere RESEARCH EXPERIENCE ✓ Instant access to resources ✓ Faster model training and iteration ✓ Unified data engineering and ML ✓ Easier to onboard team members DEPLOYMENT EXPERIENCE ✓ Easier to scale out compute ✓ Faster deployment of models ✓ Better reproducibility and isolation ✓ Flexible deployment targets BENEFITS OF A CLOUD-NATIVE ML PLATFORM

26. Confidential-Restricted – For Discussion Purposes Only 26 © Cloudera, Inc. All rights reserved. What capability of Cloudera ML is most interesting for your workloads? • Simple installation and management • Autoscaling in the cloud • Dependency management for Spark • Distributed GPU training • Cloud experience on-premise • Potential multi-tenancy with other Kubernetes workloads POLL QUESTION 2

28. THANK YOU

29. 29 © Cloudera, Inc. All rights reserved. Kubernetes CMLCML Engine Spark Driver Spark Executor Logging Logging Project Volume Project Volume Spark Executor Project Volume Hive, HDFS, HMS, Sentry, Navigator - Create user service accounts and namespace for quota management & security - Configure Spark driver / executor configuration to connect to K8S API - Populate kerberos related information for accessing HDFS and other CDH services - Populate dependency management related configurations using pod template: image, volumes, CPU/GPU/memory resources, etc - Configure Spark logging and metric information to push to external storage HOW DOES SPARK IN CML WORK?